| Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks M Engelcke, D Rao, DZ Wang, CH Tong, I Posner 2017 IEEE International Conference on Robotics and Automation (ICRA), 1355-1361, 2017 | 586 | 2017 |
| Voting for Voting in Online Point Cloud Object Detection DZ Wang, I Posner Robotics: Science and Systems, 2015 | 397 | 2015 |
| Maximum Entropy Deep Inverse Reinforcement Learning M Wulfmeier, P Ondruska, I Posner CoRR 2015, 2015 | 361 | 2015 |
| The oxford radar robotcar dataset: A radar extension to the oxford robotcar dataset D Barnes, M Gadd, P Murcutt, P Newman, I Posner 2020 IEEE International Conference on Robotics and Automation (ICRA), 6433-6438, 2020 | 274 | 2020 |
| Deep tracking: Seeing beyond seeing using recurrent neural networks P Ondruska, I Posner Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 259 | 2016 |
| Sequential attend, infer, repeat: Generative modelling of moving objects A Kosiorek, H Kim, YW Teh, I Posner Advances in Neural Information Processing Systems 31, 2018 | 220 | 2018 |
| Genesis: Generative scene inference and sampling with object-centric latent representations M Engelcke, AR Kosiorek, OP Jones, I Posner arXiv preprint arXiv:1907.13052, 2019 | 208 | 2019 |
| Navigating, recognizing and describing urban spaces with vision and lasers P Newman, G Sibley, M Smith, M Cummins, A Harrison, C Mei, I Posner, ... The International Journal of Robotics Research 28 (11-12), 1406-1433, 2009 | 182 | 2009 |
| On the limitations of representing functions on sets E Wagstaff, F Fuchs, M Engelcke, I Posner, MA Osborne International Conference on Machine Learning, 6487-6494, 2019 | 164 | 2019 |
| Toward automated driving in cities using close-to-market sensors: An overview of the v-charge project P Furgale, U Schwesinger, M Rufli, W Derendarz, H Grimmett, ... 2013 IEEE Intelligent Vehicles Symposium (IV), 809-816, 2013 | 160 | 2013 |
| Large-scale cost function learning for path planning using deep inverse reinforcement learning M Wulfmeier, D Rao, DZ Wang, P Ondruska, I Posner The International Journal of Robotics Research 36 (10), 1073-1087, 2017 | 145 | 2017 |
| What could move? finding cars, pedestrians and bicyclists in 3d laser data DZ Wang, I Posner, P Newman 2012 IEEE International Conference on Robotics and Automation, 4038-4044, 2012 | 136 | 2012 |
| Watch this: Scalable cost-function learning for path planning in urban environments M Wulfmeier, DZ Wang, I Posner 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016 | 123 | 2016 |
| Find your own way: Weakly-supervised segmentation of path proposals for urban autonomy D Barnes, W Maddern, I Posner 2017 IEEE International Conference on Robotics and Automation (ICRA), 203-210, 2017 | 119 | 2017 |
| Incremental adversarial domain adaptation for continually changing environments M Wulfmeier, A Bewley, I Posner 2018 IEEE International conference on robotics and automation (ICRA), 4489-4495, 2018 | 112 | 2018 |
| Deep tracking in the wild: End-to-end tracking using recurrent neural networks J Dequaire, P Ondrúška, D Rao, D Wang, I Posner The International Journal of Robotics Research 37 (4-5), 492-512, 2018 | 105 | 2018 |
| Model-free detection and tracking of dynamic objects with 2D lidar DZ Wang, I Posner, P Newman The International Journal of Robotics Research 34 (7), 1039-1063, 2015 | 100 | 2015 |
| A generative framework for fast urban labeling using spatial and temporal context I Posner, M Cummins, P Newman Autonomous Robots 26, 153-170, 2009 | 85 | 2009 |
| Shapestacks: Learning vision-based physical intuition for generalised object stacking O Groth, FB Fuchs, I Posner, A Vedaldi Proceedings of the european conference on computer vision (eccv), 702-717, 2018 | 83 | 2018 |
| End-to-end tracking and semantic segmentation using recurrent neural networks P Ondruska, J Dequaire, DZ Wang, I Posner arXiv preprint arXiv:1604.05091, 2016 | 82 | 2016 |
Paul NewmanProfessor University of OxfordVerified email at robots.ox.ac.uk
